The paper by Nguyen et al.1 published in this issue of Epidemiology presents a comparison of the recently suggested inverse odds ratio approach for addressing mediation and a more conventional Baron and Kenny-inspired method. Interestingly, the comparison is not done through a discussion of restrictiveness of implied assumptions, asymptotic properties, or simulations; instead, Nguyen et al.1 compare the results obtained by applying the two methods to a real-life mediation problem, which is scientifically interesting in its own right. We would like to applaud this choice as we believe it simultaneously ensures that the comparison is based on properties, which matter in actual applications, and makes the comparison accessible for a broader audience. In a wider context, the choice to stay close to real-life problems mirrors a general trend within the literature on mediation analysis namely to put more and more emphasis on ease of implementation, usability, and explanation; see, for instance, the SAS and SPSS macros by VanderWeele and Valeri 2 and the natural effects models implemented in the accompanying R package medflex by Vansteelandt and colleagues.3–5 Nguyen et al.1 also include R-code in their publications, thereby shortening the road from reading their paper to employing the considered methods on one’s own data. In this commentary, we will try to follow up on these developments by providing a snapshot of how applied mediation analysis was actually conducted in 2015. While we do not expect to find applications using the inverse odds ration approach, as it simply has not had enough time to move from theoretical concept to published applied paper, we do expect to be able to judge the willingness of authors and journals to employ the causal inference-based approach to mediation analyses. Our hope is that the snapshot will serve to illuminate whether further studies like Nguyen et al.1 are needed or if blind spots have appeared in the methodological community.
As we could not survey all journals within epidemiology, we instead chose to focus on the top five journals according to the 2014 Journal Citation Reports by Thomson Reuters. Accordingly, we surveyed the following journals: International Journal of Epidemiology, Epidemiologic Reviews, Epidemiology, European Journal of Epidemiology, and American Journal of Epidemiology. In addition, we would like to briefly examine how applied mediation analyses were conducted in more clinical journals. We therefore also included the Lancet family of journals and the New England Journal of Medicine. These journals were chosen because of their high impact and prestige. The commentary is structured as follows: First, we discuss insight we had hoped the applied communities have learned from causal inference-based mediation analysis. Second, we present the results of the review as well as the methodology employed. Finally, we provide our hopes for the future.